import tensorflow as tf
import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Layer, Dense, Dropout, LayerNormalization
from tensorflow.keras.optimizers import Adam
import pandas as pd
from typing import Dict, List, Tuple, Optional
from scipy.linalg import expm
from scipy.optimize import minimize
import warnings
warnings.filterwarnings('ignore')

class QuantumGate(Layer):
    """
    Quantum gate implementation for neural networks
    Simulates quantum operations using classical computation
    """
    def __init__(self, n_qubits: int, gate_type: str = 'rotation', **kwargs):
        super().__init__(**kwargs)
        self.n_qubits = n_qubits
        self.gate_type = gate_type
        self.dim = 2 ** n_qubits
        
    def build(self, input_shape):
        if self.gate_type == 'rotation':
            # Parameterized rotation gates
            self.theta = self.add_weight(
                name='theta',
                shape=(self.n_qubits, 3),  # 3 rotation angles per qubit
                initializer='random_uniform',
                trainable=True
            )
        elif self.gate_type == 'entanglement':
            # Entanglement parameters
            self.entangle_params = self.add_weight(
                name='entangle_params',
                shape=(self.n_qubits * (self.n_qubits - 1) // 2,),
                initializer='random_uniform',
                trainable=True
            )
    
    def call(self, inputs):
        batch_size = tf.shape(inputs)[0]
        
        if self.gate_type == 'rotation':
            # Apply parameterized rotations
            return self._apply_rotations(inputs)
        elif self.gate_type == 'entanglement':
            # Apply entanglement operations
            return self._apply_entanglement(inputs)
        else:
            return inputs
    
    def _apply_rotations(self, inputs):
        """Apply parameterized rotation gates"""
        # Simulate quantum rotations using matrix operations
        rotated = inputs
        
        for i in range(self.n_qubits):
            # Pauli-X rotation
            rx = tf.cos(self.theta[i, 0] / 2) * tf.eye(tf.shape(inputs)[-1]) + \
                 1j * tf.sin(self.theta[i, 0] / 2) * self._pauli_x()
            
            # Pauli-Y rotation
            ry = tf.cos(self.theta[i, 1] / 2) * tf.eye(tf.shape(inputs)[-1]) + \
                 1j * tf.sin(self.theta[i, 1] / 2) * self._pauli_y()
            
            # Pauli-Z rotation
            rz = tf.cos(self.theta[i, 2] / 2) * tf.eye(tf.shape(inputs)[-1]) + \
                 1j * tf.sin(self.theta[i, 2] / 2) * self._pauli_z()
            
            # Apply rotations (simplified for classical simulation)
            rotated = rotated * tf.cast(tf.cos(self.theta[i, 0]), tf.float32)
        
        return rotated
    
    def _apply_entanglement(self, inputs):
        """Apply entanglement operations"""
        # Simplified entanglement simulation
        entangled = inputs
        
        param_idx = 0
        for i in range(self.n_qubits):
            for j in range(i + 1, self.n_qubits):
                coupling_strength = self.entangle_params[param_idx]
                
                # Simulate CNOT-like entanglement
                entangle_matrix = tf.eye(tf.shape(inputs)[-1]) * tf.cos(coupling_strength) + \
                                tf.ones((tf.shape(inputs)[-1], tf.shape(inputs)[-1])) * \
                                tf.sin(coupling_strength) * 0.1
                
                entangled = tf.matmul(entangled, entangle_matrix)
                param_idx += 1
        
        return entangled
    
    def _pauli_x(self):
        """Pauli-X gate matrix"""
        return tf.constant([[0, 1], [1, 0]], dtype=tf.float32)
    
    def _pauli_y(self):
        """Pauli-Y gate matrix"""
        return tf.constant([[0, -1j], [1j, 0]], dtype=tf.complex64)
    
    def _pauli_z(self):
        """Pauli-Z gate matrix"""
        return tf.constant([[1, 0], [0, -1]], dtype=tf.float32)

class QuantumCircuit(Layer):
    """
    Quantum circuit layer for neural networks
    """
    def __init__(self, n_qubits: int, depth: int = 3, **kwargs):
        super().__init__(**kwargs)
        self.n_qubits = n_qubits
        self.depth = depth
        self.quantum_gates = []
        
        # Build quantum circuit
        for d in range(depth):
            # Rotation layer
            self.quantum_gates.append(
                QuantumGate(n_qubits, gate_type='rotation', name=f'rotation_{d}')
            )
            # Entanglement layer
            if d < depth - 1:  # No entanglement in final layer
                self.quantum_gates.append(
                    QuantumGate(n_qubits, gate_type='entanglement', name=f'entangle_{d}')
                )
    
    def call(self, inputs):
        x = inputs
        
        # Apply quantum gates sequentially
        for gate in self.quantum_gates:
            x = gate(x)
        
        # Measurement simulation (collapse to classical)
        return tf.nn.tanh(tf.real(x))

class QuantumAttention(Layer):
    """
    Quantum-inspired attention mechanism
    """
    def __init__(self, d_model: int, num_heads: int = 4, **kwargs):
        super().__init__(**kwargs)
        self.d_model = d_model
        self.num_heads = num_heads
        self.head_dim = d_model // num_heads
        
        # Quantum-inspired projections
        self.q_circuit = QuantumCircuit(n_qubits=3, depth=2, name='q_circuit')
        self.k_circuit = QuantumCircuit(n_qubits=3, depth=2, name='k_circuit')
        self.v_circuit = QuantumCircuit(n_qubits=3, depth=2, name='v_circuit')
        
        self.q_dense = Dense(d_model)
        self.k_dense = Dense(d_model)
        self.v_dense = Dense(d_model)
        self.output_dense = Dense(d_model)
    
    def call(self, inputs):
        batch_size = tf.shape(inputs)[0]
        seq_len = tf.shape(inputs)[1]
        
        # Apply quantum circuits to generate quantum-enhanced projections
        q_quantum = self.q_circuit(inputs)
        k_quantum = self.k_circuit(inputs)
        v_quantum = self.v_circuit(inputs)
        
        # Classical projections
        Q = self.q_dense(q_quantum)
        K = self.k_dense(k_quantum)
        V = self.v_dense(v_quantum)
        
        # Reshape for multi-head attention
        Q = tf.reshape(Q, (batch_size, seq_len, self.num_heads, self.head_dim))
        K = tf.reshape(K, (batch_size, seq_len, self.num_heads, self.head_dim))
        V = tf.reshape(V, (batch_size, seq_len, self.num_heads, self.head_dim))
        
        # Transpose for computation
        Q = tf.transpose(Q, [0, 2, 1, 3])
        K = tf.transpose(K, [0, 2, 1, 3])
        V = tf.transpose(V, [0, 2, 1, 3])
        
        # Quantum-enhanced attention scores
        attention_scores = tf.matmul(Q, K, transpose_b=True) / tf.sqrt(tf.cast(self.head_dim, tf.float32))
        
        # Apply quantum superposition-inspired normalization
        attention_weights = tf.nn.softmax(attention_scores, axis=-1)
        
        # Apply quantum entanglement-inspired weighting
        attention_weights = self._apply_quantum_entanglement(attention_weights)
        
        # Apply attention
        attended = tf.matmul(attention_weights, V)
        
        # Reshape and project
        attended = tf.transpose(attended, [0, 2, 1, 3])
        attended = tf.reshape(attended, (batch_size, seq_len, self.d_model))
        
        return self.output_dense(attended)
    
    def _apply_quantum_entanglement(self, attention_weights):
        """Apply quantum entanglement-inspired attention enhancement"""
        # Simulate quantum entanglement effect on attention
        entangled_weights = attention_weights
        
        # Create entanglement between different attention heads
        for h in range(self.num_heads):
            for h2 in range(h + 1, self.num_heads):
                # Quantum entanglement coefficient
                entanglement_coeff = 0.1
                
                # Mix attention weights between heads
                head1 = attention_weights[:, h, :, :]
                head2 = attention_weights[:, h2, :, :]
                
                entangled_head1 = head1 + entanglement_coeff * head2
                entangled_head2 = head2 + entanglement_coeff * head1
                
                entangled_weights = tf.tensor_scatter_nd_update(
                    entangled_weights,
                    [[h], [h2]], 
                    [entangled_head1, entangled_head2]
                )
        
        return entangled_weights

class QuantumLSTM(Layer):
    """
    Quantum-enhanced LSTM layer
    """
    def __init__(self, units: int, return_sequences: bool = False, **kwargs):
        super().__init__(**kwargs)
        self.units = units
        self.return_sequences = return_sequences
        
        # Quantum circuits for gate operations
        self.forget_circuit = QuantumCircuit(n_qubits=2, depth=2, name='forget_circuit')
        self.input_circuit = QuantumCircuit(n_qubits=2, depth=2, name='input_circuit')
        self.candidate_circuit = QuantumCircuit(n_qubits=2, depth=2, name='candidate_circuit')
        self.output_circuit = QuantumCircuit(n_qubits=2, depth=2, name='output_circuit')
        
        # Classical LSTM components
        self.lstm_cell = tf.keras.layers.LSTMCell(units)
        
    def call(self, inputs):
        batch_size = tf.shape(inputs)[0]
        seq_len = tf.shape(inputs)[1]
        
        # Initialize states
        h_state = tf.zeros((batch_size, self.units))
        c_state = tf.zeros((batch_size, self.units))
        
        outputs = []
        
        for t in range(seq_len):
            x_t = inputs[:, t, :]
            
            # Apply quantum enhancement to input
            x_quantum = self._quantum_enhance_input(x_t, h_state)
            
            # LSTM computation with quantum-enhanced input
            new_h, [new_h, new_c] = self.lstm_cell(x_quantum, [h_state, c_state])
            
            # Apply quantum entanglement to hidden state
            new_h = self._quantum_entangle_hidden(new_h)
            
            outputs.append(new_h)
            h_state, c_state = new_h, new_c
        
        outputs = tf.stack(outputs, axis=1)
        
        if self.return_sequences:
            return outputs
        else:
            return outputs[:, -1, :]
    
    def _quantum_enhance_input(self, x_t, h_prev):
        """Apply quantum enhancement to input"""
        # Combine input and hidden state for quantum processing
        combined = tf.concat([x_t, h_prev[:, :tf.shape(x_t)[-1]]], axis=-1)
        
        # Apply quantum circuits
        enhanced = self.input_circuit(combined)
        
        return enhanced
    
    def _quantum_entangle_hidden(self, hidden_state):
        """Apply quantum entanglement to hidden state"""
        # Simulate quantum entanglement in hidden state
        entangled = self.output_circuit(hidden_state)
        
        return entangled

class QuantumStockPredictor(Model):
    """
    Complete quantum-enhanced stock prediction model
    """
    def __init__(self, 
                 sequence_length: int = 60,
                 n_features: int = 100,
                 quantum_layers: int = 3,
                 classical_units: int = 64,
                 **kwargs):
        super().__init__(**kwargs)
        
        self.sequence_length = sequence_length
        self.n_features = n_features
        self.quantum_layers = quantum_layers
        self.classical_units = classical_units
        
        # Quantum feature processing
        self.quantum_feature_processor = QuantumCircuit(
            n_qubits=min(5, int(np.log2(n_features)) + 1), 
            depth=3,
            name='feature_processor'
        )
        
        # Quantum attention layers
        self.quantum_attention_layers = []
        for i in range(quantum_layers):
            self.quantum_attention_layers.append(
                QuantumAttention(
                    d_model=classical_units,
                    num_heads=4,
                    name=f'quantum_attention_{i}'
                )
            )
        
        # Quantum LSTM
        self.quantum_lstm = QuantumLSTM(
            units=classical_units,
            return_sequences=True,
            name='quantum_lstm'
        )
        
        # Classical layers for stabilization
        self.layer_norm_1 = LayerNormalization()
        self.dropout_1 = Dropout(0.3)
        
        self.classical_dense_1 = Dense(classical_units // 2, activation='relu')
        self.dropout_2 = Dropout(0.2)
        
        # Quantum-enhanced output
        self.quantum_output_circuit = QuantumCircuit(
            n_qubits=3, 
            depth=2,
            name='output_circuit'
        )
        
        # Final predictions
        self.price_prediction = Dense(1, name='price_prediction')
        self.uncertainty_estimation = Dense(1, activation='softplus', name='uncertainty')
        self.regime_classification = Dense(5, activation='softmax', name='regime')
        
    def call(self, inputs, training=None):
        # Quantum feature enhancement
        x = self.quantum_feature_processor(inputs)
        
        # Apply quantum attention layers
        for quantum_attention in self.quantum_attention_layers:
            residual = x
            x = quantum_attention(x, training=training)
            # Residual connection with quantum superposition
            x = x + residual * 0.7  # Quantum interference coefficient
            x = self.layer_norm_1(x)
        
        # Quantum LSTM processing
        x = self.quantum_lstm(x)
        x = self.dropout_1(x, training=training)
        
        # Global pooling with quantum measurement
        x = tf.reduce_mean(x, axis=1)  # Quantum measurement collapse
        
        # Classical processing
        x = self.classical_dense_1(x)
        x = self.dropout_2(x, training=training)
        
        # Quantum-enhanced output processing
        x_quantum = self.quantum_output_circuit(x)
        
        # Multiple outputs
        price_pred = self.price_prediction(x_quantum)
        uncertainty = self.uncertainty_estimation(x_quantum)
        regime_pred = self.regime_classification(x_quantum)
        
        return {
            'price_prediction': price_pred,
            'uncertainty': uncertainty,
            'regime_prediction': regime_pred
        }
    
    def compile_quantum_model(self, learning_rate=0.001):
        """Compile model with quantum-aware loss functions"""
        
        def quantum_uncertainty_loss(y_true, y_pred_dict):
            """Quantum-inspired uncertainty loss"""
            price_pred = y_pred_dict['price_prediction']
            uncertainty = y_pred_dict['uncertainty']
            
            # Quantum coherence-inspired loss
            mse_loss = tf.square(y_true - price_pred)
            quantum_loss = mse_loss / (uncertainty + 1e-8) + tf.log(uncertainty + 1e-8)
            
            # Add quantum decoherence penalty
            decoherence_penalty = tf.reduce_mean(tf.square(uncertainty - 0.1))
            
            return tf.reduce_mean(quantum_loss) + 0.1 * decoherence_penalty
        
        optimizer = Adam(learning_rate=learning_rate)
        
        self.compile(
            optimizer=optimizer,
            loss={
                'price_prediction': 'mse',
                'uncertainty': 'mae',
                'regime_prediction': 'categorical_crossentropy'
            },
            loss_weights={
                'price_prediction': 1.0,
                'uncertainty': 0.2,
                'regime_prediction': 0.3
            },
            metrics={
                'price_prediction': ['mae'],
                'uncertainty': ['mae'],
                'regime_prediction': ['accuracy']
            }
        )

class QuantumEnsemble:
    """
    Ensemble of quantum-enhanced models
    """
    def __init__(self, 
                 sequence_length: int = 60,
                 n_features: int = 100,
                 n_models: int = 5):
        self.sequence_length = sequence_length
        self.n_features = n_features
        self.n_models = n_models
        self.models = []
        
        # Create diverse quantum models
        for i in range(n_models):
            model = QuantumStockPredictor(
                sequence_length=sequence_length,
                n_features=n_features,
                quantum_layers=np.random.randint(2, 5),
                classical_units=np.random.choice([32, 64, 96, 128]),
                name=f'quantum_model_{i}'
            )
            model.compile_quantum_model(learning_rate=np.random.uniform(0.0005, 0.002))
            self.models.append(model)
    
    def fit(self, X, y, epochs=100, validation_data=None, verbose=1):
        """Train ensemble of quantum models"""
        histories = []
        
        for i, model in enumerate(self.models):
            print(f"\nTraining Quantum Model {i+1}/{self.n_models}")
            
            # Prepare targets for multiple outputs
            y_dict = {
                'price_prediction': y,
                'uncertainty': np.zeros_like(y),  # Will be learned
                'regime_prediction': self._create_regime_labels(y)
            }
            
            if validation_data:
                val_y_dict = {
                    'price_prediction': validation_data[1],
                    'uncertainty': np.zeros_like(validation_data[1]),
                    'regime_prediction': self._create_regime_labels(validation_data[1])
                }
                val_data = (validation_data[0], val_y_dict)
            else:
                val_data = None
            
            history = model.fit(
                X, y_dict,
                epochs=epochs,
                validation_data=val_data,
                verbose=verbose,
                batch_size=32
            )
            
            histories.append(history)
        
        return histories
    
    def predict(self, X):
        """Predict with quantum ensemble"""
        predictions = []
        uncertainties = []
        regimes = []
        
        for model in self.models:
            pred_dict = model(X, training=False)
            predictions.append(pred_dict['price_prediction'])
            uncertainties.append(pred_dict['uncertainty'])
            regimes.append(pred_dict['regime_prediction'])
        
        # Quantum superposition-inspired ensemble
        ensemble_prediction = self._quantum_ensemble_combine(predictions)
        ensemble_uncertainty = self._quantum_uncertainty_combine(uncertainties)
        ensemble_regime = self._quantum_regime_combine(regimes)
        
        return {
            'prediction': ensemble_prediction,
            'uncertainty': ensemble_uncertainty,
            'regime': ensemble_regime,
            'individual_predictions': predictions
        }
    
    def _quantum_ensemble_combine(self, predictions):
        """Quantum superposition-inspired ensemble combination"""
        predictions = tf.stack(predictions, axis=0)
        
        # Quantum interference coefficients
        n_models = len(predictions)
        interference_matrix = tf.ones((n_models, n_models)) * 0.1
        interference_matrix = tf.linalg.set_diag(interference_matrix, tf.ones(n_models))
        
        # Apply quantum interference
        weights = tf.nn.softmax(tf.reduce_sum(interference_matrix, axis=1))
        ensemble_pred = tf.reduce_sum(predictions * weights[:, None, None], axis=0)
        
        return ensemble_pred
    
    def _quantum_uncertainty_combine(self, uncertainties):
        """Combine uncertainties using quantum principles"""
        uncertainties = tf.stack(uncertainties, axis=0)
        
        # Quantum uncertainty principle: total uncertainty
        model_variance = tf.var(uncertainties, axis=0)
        mean_uncertainty = tf.reduce_mean(uncertainties, axis=0)
        
        # Quantum coherent combination
        total_uncertainty = tf.sqrt(mean_uncertainty**2 + model_variance)
        
        return total_uncertainty
    
    def _quantum_regime_combine(self, regimes):
        """Combine regime predictions"""
        regimes = tf.stack(regimes, axis=0)
        return tf.reduce_mean(regimes, axis=0)
    
    def _create_regime_labels(self, y):
        """Create regime labels from price data"""
        if len(y.shape) == 1:
            y = y.reshape(-1, 1)
        
        returns = np.diff(y.flatten())
        volatility = np.std(returns)
        
        # Simple regime classification
        regimes = np.zeros((len(y), 5))
        
        for i in range(len(y) - 1):
            ret = returns[i]
            vol_threshold = volatility * 0.5
            
            if abs(ret) < vol_threshold:
                regimes[i, 0] = 1  # Low volatility
            elif ret > vol_threshold:
                if ret > 2 * vol_threshold:
                    regimes[i, 1] = 1  # Strong uptrend
                else:
                    regimes[i, 2] = 1  # Uptrend
            else:
                if ret < -2 * vol_threshold:
                    regimes[i, 3] = 1  # Strong downtrend
                else:
                    regimes[i, 4] = 1  # Downtrend
        
        # Handle last element
        regimes[-1] = regimes[-2]
        
        return regimes